01_lab_R_learning--1-
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Subject
Mathematics
Date
Apr 3, 2024
Type
docx
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Uploaded by htk236
Lab 01 R Learning
Kelly
2020-03-26
Logical Operators:
1.
Use logical operations to get R to agree that “two plus two equals 5” is FALSE.
2
+
2
==
5
## [1] FALSE
2.
Use logical operations to test whether 8 ^ 13 is less than 15 ^ 9.
8
^
13
<
15
^
9
## [1] FALSE
Variables:
3.
Create a variable called potato whose value corresponds to the number of potatoes you’ve eaten in the last week. Or something equally ridiculous. Print out the value of potato.
potato=
88
potato
## [1] 88
4.
Calculate the square root of potato using the sqrt() function. Print out the value of potato again to verify that the value of potato hasn’t changed.
sqrt
(potato)
## [1] 9.380832
potato
## [1] 88
5.
Reassign the value of potato to potato * 2. Print out the new value of potato to verify that it has changed.
potato =
potato
*
2
potato
## [1] 176
6.
Try making a character (string) variable and a logical variable . Try creating a variable with a “missing” value NA. You can call these variables whatever you would like. Use class(variablename) to make sure they are the right type of variable.
city =
"age"
class
(city)
## [1] "character"
areyoufat =
FALSE
class
(areyoufat)
## [1] "logical"
email=
NA
class
(email)
## [1] "logical"
Vectors:
7.
Create a numeric vector with three elements using c().
a=
c
(
3
,
4
,
5
)
a
## [1] 3 4 5
8.
Create a character vector with three elements using c().
myfavfood=
c
(
"icecream"
,
"candy"
,
"cake"
)
myfavfood
## [1] "icecream" "candy" "cake"
9.
Create a numeric vector called age whose elements contain the ages of three people you know, where the names of each element correspond to the names of those people.
age=
c
(
25
,
55
,
60
)
names
(age)=
c
(
"kelly"
,
"stanley"
,
"cora"
)
age
## kelly stanley cora ## 25 55 60
10.
Use “indexing by number” to get R to print out the first element of one of the vectors you created in the last questions.
age[
1
]
## kelly ## 25
11.
Use logical indexing to return all the ages of all people in age greater than 20.
age
>
20
## kelly stanley cora ## TRUE TRUE TRUE
12.
Use indexing by name to return the age of one of the people whose ages you’ve stored in age
age[
"kelly"
]
## kelly ## 25
Matrices:
Dataframes:
13.
Load the airquality dataset.
14.
Use the $ method to print out the Wind variable in airquality.
15.
Print out the third element of the Wind variable.
airquality
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## 11 7 NA 6.9 74 5 11
## 12 16 256 9.7 69 5 12
## 13 11 290 9.2 66 5 13
## 14 14 274 10.9 68 5 14
## 15 18 65 13.2 58 5 15
## 16 14 334 11.5 64 5 16
## 17 34 307 12.0 66 5 17
## 18 6 78 18.4 57 5 18
## 19 30 322 11.5 68 5 19
## 20 11 44 9.7 62 5 20
## 21 1 8 9.7 59 5 21
## 22 11 320 16.6 73 5 22
## 23 4 25 9.7 61 5 23
## 24 32 92 12.0 61 5 24
## 25 NA 66 16.6 57 5 25
## 26 NA 266 14.9 58 5 26
## 27 NA NA 8.0 57 5 27
## 28 23 13 12.0 67 5 28
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## 29 45 252 14.9 81 5 29
## 30 115 223 5.7 79 5 30
## 31 37 279 7.4 76 5 31
## 32 NA 286 8.6 78 6 1
## 33 NA 287 9.7 74 6 2
## 34 NA 242 16.1 67 6 3
## 35 NA 186 9.2 84 6 4
## 36 NA 220 8.6 85 6 5
## 37 NA 264 14.3 79 6 6
## 38 29 127 9.7 82 6 7
## 39 NA 273 6.9 87 6 8
## 40 71 291 13.8 90 6 9
## 41 39 323 11.5 87 6 10
## 42 NA 259 10.9 93 6 11
## 43 NA 250 9.2 92 6 12
## 44 23 148 8.0 82 6 13
## 45 NA 332 13.8 80 6 14
## 46 NA 322 11.5 79 6 15
## 47 21 191 14.9 77 6 16
## 48 37 284 20.7 72 6 17
## 49 20 37 9.2 65 6 18
## 50 12 120 11.5 73 6 19
## 51 13 137 10.3 76 6 20
## 52 NA 150 6.3 77 6 21
## 53 NA 59 1.7 76 6 22
## 54 NA 91 4.6 76 6 23
## 55 NA 250 6.3 76 6 24
## 56 NA 135 8.0 75 6 25
## 57 NA 127 8.0 78 6 26
## 58 NA 47 10.3 73 6 27
## 59 NA 98 11.5 80 6 28
## 60 NA 31 14.9 77 6 29
## 61 NA 138 8.0 83 6 30
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 65 NA 101 10.9 84 7 4
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 72 NA 139 8.6 82 7 11
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 75 NA 291 14.9 91 7 14
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 83 NA 258 9.7 81 7 22
## 84 NA 295 11.5 82 7 23
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 39 83 6.9 81 8 1
## 94 9 24 13.8 81 8 2
## 95 16 77 7.4 82 8 3
## 96 78 NA 6.9 86 8 4
## 97 35 NA 7.4 85 8 5
## 98 66 NA 4.6 87 8 6
## 99 122 255 4.0 89 8 7
## 100 89 229 10.3 90 8 8
## 101 110 207 8.0 90 8 9
## 102 NA 222 8.6 92 8 10
## 103 NA 137 11.5 86 8 11
## 104 44 192 11.5 86 8 12
## 105 28 273 11.5 82 8 13
## 106 65 157 9.7 80 8 14
## 107 NA 64 11.5 79 8 15
## 108 22 71 10.3 77 8 16
## 109 59 51 6.3 79 8 17
## 110 23 115 7.4 76 8 18
## 111 31 244 10.9 78 8 19
## 112 44 190 10.3 78 8 20
## 113 21 259 15.5 77 8 21
## 114 9 36 14.3 72 8 22
## 115 NA 255 12.6 75 8 23
## 116 45 212 9.7 79 8 24
## 117 168 238 3.4 81 8 25
## 118 73 215 8.0 86 8 26
## 119 NA 153 5.7 88 8 27
## 120 76 203 9.7 97 8 28
## 121 118 225 2.3 94 8 29
## 122 84 237 6.3 96 8 30
## 123 85 188 6.3 94 8 31
## 124 96 167 6.9 91 9 1
## 125 78 197 5.1 92 9 2
## 126 73 183 2.8 93 9 3
## 127 91 189 4.6 93 9 4
## 128 47 95 7.4 87 9 5
## 129 32 92 15.5 84 9 6
## 130 20 252 10.9 80 9 7
## 131 23 220 10.3 78 9 8
## 132 21 230 10.9 75 9 9
## 133 24 259 9.7 73 9 10
## 134 44 236 14.9 81 9 11
## 135 21 259 15.5 76 9 12
## 136 28 238 6.3 77 9 13
## 137 9 24 10.9 71 9 14
## 138 13 112 11.5 71 9 15
## 139 46 237 6.9 78 9 16
## 140 18 224 13.8 67 9 17
## 141 13 27 10.3 76 9 18
## 142 24 238 10.3 68 9 19
## 143 16 201 8.0 82 9 20
## 144 13 238 12.6 64 9 21
## 145 23 14 9.2 71 9 22
## 146 36 139 10.3 81 9 23
## 147 7 49 10.3 69 9 24
## 148 14 20 16.6 63 9 25
## 149 30 193 6.9 70 9 26
## 150 NA 145 13.2 77 9 27
## 151 14 191 14.3 75 9 28
## 152 18 131 8.0 76 9 29
## 153 20 223 11.5 68 9 30
airquality
$
Wind
## [1] 7.4 8.0 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 6.9 9.7 9.2 10.9 13.2
## [16] 11.5 12.0 18.4 11.5 9.7 9.7 16.6 9.7 12.0 16.6 14.9 8.0 12.0 14.9 5.7
## [31] 7.4 8.6 9.7 16.1 9.2 8.6 14.3 9.7 6.9 13.8 11.5 10.9 9.2 8.0 13.8
## [46] 11.5 14.9 20.7 9.2 11.5 10.3 6.3 1.7 4.6 6.3 8.0 8.0 10.3 11.5 14.9
## [61] 8.0 4.1 9.2 9.2 10.9 4.6 10.9 5.1 6.3 5.7 7.4 8.6 14.3 14.9 14.9
## [76] 14.3 6.9 10.3 6.3 5.1 11.5 6.9 9.7 11.5 8.6 8.0 8.6 12.0 7.4 7.4
## [91] 7.4 9.2 6.9 13.8 7.4 6.9 7.4 4.6 4.0 10.3 8.0 8.6 11.5 11.5 11.5
## [106] 9.7 11.5 10.3 6.3 7.4 10.9 10.3 15.5 14.3 12.6 9.7 3.4 8.0 5.7 9.7
## [121] 2.3 6.3 6.3 6.9 5.1 2.8 4.6 7.4 15.5 10.9 10.3 10.9 9.7 14.9 15.5
## [136] 6.3 10.9 11.5 6.9 13.8 10.3 10.3 8.0 12.6 9.2 10.3 10.3 16.6 6.9 13.2
## [151] 14.3 8.0 11.5
airquality
$
Wind[
3
]
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## [1] 12.6
16.
Create a new data frame called aq that includes only the first 10 cases. Hint: typing c(1,2,3,4,5,6,7,8,9,10) is tedious. R allows you to use 1:10 as a shorthand method!
17.
Use logical indexing to print out all days (ie. cases) in aq where the Ozone level was higher than 20.
a.
What did the output do with NA values?
18.
Use subset() to do the same thing. Notice the difference in the output.
aq=
head
(airquality,
10
)
aq
$
Ozone
>
20
## [1] TRUE TRUE FALSE FALSE NA TRUE TRUE FALSE FALSE NA
x=
subset
(aq,Ozone
>
20
)
19.
Create a TooWindy variable inside aq, which is a logical variable that is TRUE if Wind is greater than 10, and FALSE otherwise.
aq
$
TooWindy=aq
$
Wind
>
10
aq
## Ozone Solar.R Wind Temp Month Day TooWindy
## 1 41 190 7.4 67 5 1 FALSE
## 2 36 118 8.0 72 5 2 FALSE
## 3 12 149 12.6 74 5 3 TRUE
## 4 18 313 11.5 62 5 4 TRUE
## 5 NA NA 14.3 56 5 5 TRUE
## 6 28 NA 14.9 66 5 6 TRUE
## 7 23 299 8.6 65 5 7 FALSE
## 8 19 99 13.8 59 5 8 TRUE
## 9 8 19 20.1 61 5 9 TRUE
## 10 NA 194 8.6 69 5 10 FALSE
20.
Use the length() function to determine the number of observations in the airquality dataframe.
length
(airquality)
## [1] 6
21.
Calculate the mean and standard deviation of one of the variables in airquality.
mean
(airquality
$
Temp)
## [1] 77.88235
sd
(airquality
$
Temp)
## [1] 9.46527
22.
Make a table of the Temp values.
temp=
table
(airquality
$
Temp)
temp
## ## 56 57 58 59 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 ## 1 3 2 2 3 2 1 2 2 3 4 4 3 1 3 3 5 4 4 9 7 6 6 5 11 9 ## 83 84 85 86 87 88 89 90 91 92 93 94 96 97 ## 4 5 5 7 5 3 2 3 2 5 3 2 1 1
23.
Make a histogram of the Ozone column. Is it a normal distribution? Why or why not? No, it’s not, it’s right-skewed.
hist
(airquality
$
Ozone)
Functions:
24.
Make a simple function to calculate x + 6.
addition=
function
(x){x
+
6
}
25.
Use that function add 6 to the Temp column in airquality.
addition
(airquality
$
Temp)
## [1] 73 78 80 68 62 72 71 65 67 75 80 75 72 74 64 70 72 63
## [19] 74 68 65 79 67 67 63 64 63 73 87 85 82 84 80 73 90 91
## [37] 85 88 93 96 93 99 98 88 86 85 83 78 71 79 82 83 82 82
## [55] 82 81 84 79 86 83 89 90 91 87 90 89 89 94 98 98 95 88
## [73] 79 87 97 86 87 88 90 93 91 80 87 88 92 91 88 92 94 92
## [91] 89 87 87 87 88 92 91 93 95 96 96 98 92 92 88 86 85 83
## [109] 85 82 84 84 83 78 81 85 87 92 94 103 100 102 100 97 98 99
## [127] 99 93 90 86 84 81 79 87 82 83 77 77 84 73 82 74 88 70
## [145] 77 87 75 69 76 83 81 82 74
Packages:
26.
Install the ggplot2 package.
27.
Install the car package.
28.
Install the ez package. (no output necessary for these three)
29.
Load the car library.
library
(
"car"
)
## Loading required package: carData
Files
30.
Import the csv file provided on Canvas.
library
(readr)
lab_R_learning <-
read_csv
(
"lab_R_learning.csv"
)
## Parsed with column specification:
## cols(
## variable1 = col_double(),
## stuff2 = col_character(),
## thing3 = col_logical()
## )
names
(lab_R_learning)
## [1] "variable1" "stuff2" "thing3"
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